{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7a6f48c7",
   "metadata": {},
   "source": [
    "<div><font face=\"Times New Roman\" size=7><br><br>\n",
    "<center>\n",
    "Spectral Clustering\n",
    "<center><br></div>\n",
    " \n",
    "\n",
    "### Machine Learning for Bioinformatics: Homework 1 (Practical)\n",
    "*Refer to (preferably)Quera or Alireza Gargoori for any questions you have or other inconveniences.*  <br>\n",
    "*Telegram ID: @alregamo*  <br>\n",
    "*Email: alireza.agm@gmail.com* <br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "119e75a1",
   "metadata": {},
   "source": [
    "<font face=\"Arial\" size=4><br>\n",
    "    \n",
    "One type of common methods for clustering are the <b>Spectral Clustering</b> methods, which we would learn in this execise.\n",
    "<br><br>\n",
    "Spectral Clustering is a type of clustering algorithm in machine learning that <b>uses eigenvectors of a similarity matrix</b> to divide a set of data points into clusters. The basic idea behind spectral clustering is to use the eigenvectors of the Laplacian matrix of a graph to represent the data points and find clusters by applying k-means or another clustering algorithm to the eigenvectors. <br><br>\n",
    "    \n",
    "<div> <br>\n",
    "<center>\n",
    "<img src=\"https://images.squarespace-cdn.com/content/v1/5d782753c70af105c29a9b14/1608653466446-YC3DJUQR7FDU35XM90AE/shutterstock_1410280415.jpg?format=1000w\" width=\"700\">\n",
    "</center>\n",
    "</div><br>\n",
    "      \n",
    "Consider the matrix <b>$W$</b> representing the similarity of data points, where $(i,j)$ entry is non-zero if samples $(x^{(i)}, x^{(j)})$ are in the mutual KNN (k-nearest neighbors) of each other.<br><br>\n",
    "    $$W_{ij} = \\begin{cases}\n",
    "    1, & x^{(i)} \\in KNN \\{x^{(j)}\\} \\hspace{3mm} AND \\hspace{3mm} x^{(j)} \\in KNN \\{x^{(i)}\\} \\\\\n",
    "    0, & otherwise\n",
    "    \\end{cases}\n",
    "    $$ <br><br>\n",
    "    Another method to define <b>$W$</b> is to use a fully connected graph, where the value of its entries are obtained from a function which measures the similarity of these samples.<br><br>\n",
    "    $$W_{ij} =  k(x^{(i)}, x^{(j)})$$ <br><br>\n",
    "    where $k(x^{(i)}, x^{(j)})$ is a function to measure the similarity of two samples. An example of this function could be the <b>Radial Basis Function (RBF)</b>, defined as:\n",
    "    $$k(x^{(i)}, x^{(j)}) = exp(-\\gamma \\lVert x^{(i)} - x^{(j)} \\rVert)$$\n",
    "   <br> We call matrix <b>$W$</b> the adjacency matrix. (There are other types of definitions for this matrix, like the $\\epsilon$-neighborhood graph, simple KNN graph, etc.) <br><br>\n",
    "    The degree of each vertex in the graph is defined as $g_i = \\sum_{j}^{} w_{ij}$.\n",
    "    Also, consider the diagonal matrix <b>$G$</b>, defined as: <br><br>\n",
    "    $$G_{ij} = \\begin{cases}\n",
    "    g_i, & i=j \\\\\n",
    "    0, & otherwise\n",
    "    \\end{cases}\n",
    "    $$ <br><br>\n",
    "    Now we are ready to define the <b>Laplacian</b> matrix <b>$L$</b>:\n",
    "    $$L = G - W $$<br><br><br>\n",
    "    It can be shown the eigenvectors of <b>$L$</b> corresponding to the 𝑚 smallest eigenvalues are appropriate for clustering. In other words, we first compute the 𝑚 smallest eigenvalues $\\lambda_i$ and their corresponding eigenvectors $\\phi_i$. Let $\\Phi \\in \\mathbb{R} ^{p\\times m}$ be a matrix consisting of $\\{\\phi_i\\}_{i=1}^{m}$, i.e. the first(smallest)  eigenvectors of <b>$L$</b>:<br><br>\n",
    "    $$\\Phi(x^{(i)}) = [\\phi_1(x^{(i)}), \\phi_2(x^{(i)}), ..., \\phi_m(x^{(i)})]^T \\in \\mathbb{R}^m$$ <br>\n",
    "    In other words, we transform the original data $x^{(i)}$ from $\\mathbb{R}^{p}$ to $\\mathbb{R}^{m}$ through the first m eigenvectors of <b>$L$</b>: it is a nonlinear transformation. The $\\it{i}$th row of $\\Phi$ represents the $\\it{i}$th data point in the new feature space. This step is also called Laplacian eigenmap, which is the key step in spectral clustering.\n",
    "    <br><br> Now, in the final step, we need to apply K-means clustering to the rows of $\\Phi$ to group the data into m clusters."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71c693f8",
   "metadata": {},
   "source": [
    "<font face=\"Arial\" size=4>\n",
    "<br>In summary, the procedure consists of 4 steps: <br><br>\n",
    "    <b>1.</b> Constuct the adjacency matrix <b>$W$</b>. <br> <br>\n",
    "    <b>2.</b> Find the corresponding laplacian matrix <b>$L$</b>. <br><br>\n",
    "    <b>3.</b> Find the m smallest eigenvalues and their corresponding eigenvectors $\\{\\phi_i\\}_{i=1}^{m}$. Transform the samples in the original feature space into the new one, using the matrix $\\Phi$. <br><br>\n",
    "    <b>4.</b> Apply K-Means in this new feature space. <br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94f54e69",
   "metadata": {},
   "source": [
    "## Part (A) (Bonus)\n",
    "<font face=\"Arial\" size=4>\n",
    "<br>Prove that Laplacian Matrix is positive semi-definite. (Therefore, all the eigenvalues of <b>$L$</b> are $\\geq 0$)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a74a0d55",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Your Answer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68581469",
   "metadata": {},
   "source": [
    "## Part (B)\n",
    "<font face=\"Arial\" size=4> <br>\n",
    "We stated that the transformation must be done using the eigenvectos corresponding to the smallest eigenvalues of laplacian matrix. This can be proved mathematically; however, we want to illustrate it with a simple example. <br>\n",
    "\n",
    "It can be shown that as the eigenvalues of the laplacian matrix gets closer to zero, the graph is more disconnected! <br>\n",
    "Explain the above statement in the following gif. In other words, explain the effect of adding edges to the graph on the eigenvalues of the laplacian matrix:\n",
    "<div> <br>\n",
    "<center>\n",
    "<img src=\"spectral.gif\" width=\"700\">\n",
    "</center>\n",
    "</div><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90303dcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Your Answer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "370984ae",
   "metadata": {},
   "source": [
    "<font face=\"Arial\" size=4>\n",
    "Why do you think the second eigenvalue is close to zero in the following graph?\n",
    "<div> <br>\n",
    "<center>\n",
    "<img src=\"spectral_final.png\" width=\"700\">\n",
    "</center>\n",
    "</div><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7db3a5dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Your Answer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb5aa127",
   "metadata": {},
   "source": [
    "## Part (C)\n",
    "<font face=\"Arial\" size=4> <br>\n",
    "Here we want to compare the results of spectral clustering to the typical K-Means clustering. Consider the following dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ec14c46f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_circles\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "X, labels = make_circles(500, factor=0.2, noise=0.1, random_state=1)\n",
    "\n",
    "plt.figure(figsize=(8,8))\n",
    "scatter = plt.scatter(X[:, 0], X[:, 1], marker='o', c=labels, s=100, cmap='coolwarm', alpha=0.75)\n",
    "plt.legend(handles=scatter.legend_elements()[0], labels=['Class 1','Class 0'], fontsize='x-large', markerscale=1.5)\n",
    "plt.xlabel(\"$x_1$\")\n",
    "plt.ylabel(\"$x_2$\")\n",
    "plt.title(\"Scatter Plot of the Dataset\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fdfa7af",
   "metadata": {},
   "source": [
    "<font face=\"Arial\" size=4>\n",
    "Assume that the true labels are unknown, and we need to cluster the samples into two clusters. Apply K-Means algorithm with $K=2$ on the samples and plot the resulting clusters. <br> What do you think about the clusters from K-Means? Do the results match with the true labels?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0ea26c6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "## Your Code"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31e459f1",
   "metadata": {},
   "source": [
    "<font face=\"Arial\" size=4> <br>\n",
    "Now apply spectral clustering method on this dataset. You can use <code>sklearn.cluster.SpectralClustering</code> in this part. Also, use the KNN adjacency matrix for <b>$W$</b>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3134a076",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import SpectralClustering\n",
    "\n",
    "## Your Code"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b74d5245",
   "metadata": {},
   "source": [
    "<font face=\"Arial\" size=4> <br>\n",
    "Change the adjacency matrix as the fully connected graph with rbf as the similarity measurement function. (Note that you can set this through the <code>affinity</code> parameter of the spectral clustering model.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "99e5ff57",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Your Code"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1af1e124",
   "metadata": {},
   "source": [
    "<font face=\"Arial\" size=4> <br>\n",
    "\n",
    "Compare the results of K-Means model vs. Spectral Clustering methods. Also, mention the effect of <code>rbf</code> adjacency matrix on the result of spectral clustering model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7b38c893",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Your Answer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "356d4166",
   "metadata": {},
   "source": [
    "# Part (D)\n",
    "\n",
    "<font face=\"Arial\" size=4><br>\n",
    "Explain the pros and cons of spectral clustering methods. (Feel free to search more about its advantages and disadvantages through internet, but mention your sources.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e065db0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Your Answer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25355292",
   "metadata": {},
   "source": [
    "# Part (E): Clustering on Cancer Gene Expression RNA-seq dataset\n",
    "\n",
    "### Part (E.1): Clustering\n",
    "<font face=\"Arial\" size=4><br>\n",
    "This dataset is from the UCI Machine Learning repository. This collection of data is part of the RNA-Seq (HiSeq) PANCAN data set. It is a random extraction of gene expressions of patients having different types of tumor: \n",
    "    \n",
    "BRCA (breast invasive carcinoma) <br>\n",
    "KIRC (kidney renal clear cell carcinoma) <br>\n",
    "COAD (colon adenocarcinoma) <br>\n",
    "LUAD (lung adenocarcinoma) <br>\n",
    "PRAD (prostate adenocarcinoma) <br> <br>\n",
    "    \n",
    "    \n",
    "There are 801 instances with 20531 attributes, which are the gene expressions among different patients with each of this tumors. The data can be downloaded here: https://archive.ics.uci.edu/ml/datasets/gene+expression+cancer+RNA-Seq"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52fc70d4",
   "metadata": {},
   "source": [
    "<font face=\"Arial\" size=4>\n",
    "Assume that the true labels are unknown and we cannot use classification methods to distinguish cancer types. Use Spectral Clustering method and try to cluster the data as best as you can. You are allowed to use <code>sklearn.cluster.SpectralClustering</code> for this purpose. The choice of hyperparameters are on your own."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c784d936",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Your Code"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db10850b",
   "metadata": {},
   "source": [
    "### Part (E.2): Evaluation of Clustering Algorithm\n",
    "\n",
    "<font face=\"Arial\" size=4><br>\n",
    "Search about the metrics that we can use for clustering purposes, given that we have the true labels of the data and we want to assess the performance of our clustering method. Give a short explanation about the <code>adjusted rand index(ARI)</code> and <code>normalized mutual information(NMI)</code> metrics and evaluate your model with these metrics. You are allowed to use <code>sklearn.metrics</code> in this part."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2024ca5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Your Answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f988e9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Your Code"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c623a64f",
   "metadata": {},
   "source": [
    "### Part (E.3): Visualization\n",
    "\n",
    "<font face=\"Arial\" size=4><br>\n",
    "Visualize the ground truth labels and the predictions of your model in 2 figures. You can use <code>PCA</code> for dimensionality reduction before visualization. Also, you can use <code>t-SNE</code> from <code>sklearn.manifold.TSNE</code> instead. Other methods such as <code>UMAP</code> are accepted as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ebecd89",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Your Code"
   ]
  }
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